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08/30/2023
Iterative Reward Shaping using Human Feedback for Correcting Reward Misspecification
A well-defined reward function is crucial for successful training of an ...
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06/14/2023
Density-Aware Reinforcement Learning to Optimise Energy Efficiency in UAV-Assisted Networks
Unmanned aerial vehicles (UAVs) serving as aerial base stations can be d...
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03/17/2023
Prevalence of Code Smells in Reinforcement Learning Projects
Reinforcement Learning (RL) is being increasingly used to learn and adap...
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03/08/2023
RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement Learning
While reinforcement learning (RL) algorithms have been successfully appl...
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03/02/2023
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning
Transfer learning in Reinforcement Learning (RL) has been widely studied...
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11/09/2022
Deep W-Networks: Solving Multi-Objective Optimisation Problems With Deep Reinforcement Learning
In this paper, we build on advances introduced by the Deep Q-Networks (D...
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11/02/2022
Causal Counterfactuals for Improving the Robustness of Reinforcement Learning
Reinforcement learning (RL) is applied in a wide variety of fields. RL e...
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10/21/2022
Counterfactual Explanations for Reinforcement Learning
While AI algorithms have shown remarkable success in various fields, the...
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09/30/2022
Communication-Enabled Multi-Agent Decentralised Deep Reinforcement Learning to Optimise Energy-Efficiency in UAV-Assisted Networks
Unmanned Aerial Vehicles (UAVs) are increasingly deployed to provide wir...
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07/18/2022
Boolean Decision Rules for Reinforcement Learning Policy Summarisation
Explainability of Reinforcement Learning (RL) policies remains a challen...
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06/24/2022
Guidelines for Artifacts to Support Industry-Relevant Research on Self-Adaptation
Artifacts support evaluating new research results and help comparing the...
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05/23/2022
FedSA: Accelerating Intrusion Detection in Collaborative Environments with Federated Simulated Annealing
Fast identification of new network attack patterns is crucial for improv...
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04/04/2022
Optimising Energy Efficiency in UAV-Assisted Networks using Deep Reinforcement Learning
In this letter, we study the energy efficiency (EE) optimisation of unma...
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03/21/2022
ReCCoVER: Detecting Causal Confusion for Explainable Reinforcement Learning
Despite notable results in various fields over the recent years, deep re...
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01/18/2022
Enabling Deep Reinforcement Learning on Energy Constrained Devices at the Edge of the Network
Deep Reinforcement Learning (DRL) solutions are becoming pervasive at th...
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12/17/2021
Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents
In complex tasks where the reward function is not straightforward and co...
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12/01/2021
Multi-Agent Transfer Learning in Reinforcement Learning-Based Ride-Sharing Systems
Reinforcement learning (RL) has been used in a range of simulated real-w...
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11/03/2021
Multi-Agent Deep Reinforcement Learning For Optimising Energy Efficiency of Fixed-Wing UAV Cellular Access Points
Unmanned Aerial Vehicles (UAVs) promise to become an intrinsic part of n...
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09/29/2021
Analyse or Transmit: Utilising Correlation at the Edge with Deep Reinforcement Learning
Millions of sensors, cameras, meters, and other edge devices are deploye...
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06/01/2021
Energy-aware placement optimization of UAV base stations via decentralized multi-agent Q-learning
Unmanned aerial vehicles serving as aerial base stations (UAV-BSs) can b...
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06/01/2021
A reinforcement learning approach to improve communication performance and energy utilization in fog-based IoT
Recent research has shown the potential of using available mobile fog de...
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03/11/2021
Adaptation to Unknown Situations as the Holy Grail of Learning-Based Self-Adaptive Systems: Research Directions
Self-adaptive systems continuously adapt to changes in their execution e...
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03/11/2021
Auto-COP: Adaptation Generation in Context-Oriented Programming using Reinforcement Learning Options
Self-adaptive software systems continuously adapt in response to interna...
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02/25/2021
Mobility for Cellular-Connected UAVs: challenges for the network provider
Unmanned Aerial Vehicle (UAV) technology is becoming more prevalent and ...
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11/06/2020
Experimental Evaluation of a UAV User QoS from a Two-Tier 3.6GHz Spectrum Network
Unmanned Aerial Vehicle (UAV) technology is becoming increasingly used i...
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10/02/2020
REQIBA: Regression and Deep Q-Learning for Intelligent UAV Cellular User to Base Station Association
Unmanned Aerial Vehicles (UAVs) are emerging as important users of next-...
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07/27/2020
Adaptive Height Optimisation for Cellular-Connected UAVs using Reinforcement Learning
With the increasing number of uav as users of the cellular network, the ...
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10/08/2018
Multi-agent Deep Reinforcement Learning for Zero Energy Communities
Advances in renewable energy generation and introduction of the governme...
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05/23/2018
